Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 2
Articles | Volume 18, issue 2
https://doi.org/10.5194/gmd-18-433-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-433-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 2
Methods for assessment of models
 | 
27 Jan 2025
Methods for assessment of models |  | 27 Jan 2025

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini

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Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
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